Impact of Electric Vehicles on Travel and Electricity Demand in Metropolitan Area： A Case Study in Nagoya

In this study, we examine the impacts that EVs （electric vehicles） have on vehicle usage patterns and environmental improvements, using our integrated travel demand forecasting model, which can simulate an individual activity-travel behavior in each time period, as well as consider an induced demand by decreasing travel cost. In order to examine the effects that charging/discharging have on the demand in electricity, we analyze scenarios based on the simulation results of the EVs＇ parking location, parking duration and the battery state of charge. From the simulation, result under the ownership rate of EVs in the Nagoya metropolitan area in 2020 is about 6%, which turns out that the total CO2 emissions have decreased by 4% although the situation of urban transport is not changed. After calculating the electricity demand in each zone using architectural area and basic units of hourly power consumption, we evaluate the effect to decrease the peak load by V2G （vehicle-to-grid）. According to the results, if EV drivers charge at home during the night and discharge at work during the day, the electricity demand in Nagoya city increases by approximately 1%, although changes in each individual zone range from -7% to ＋8%, depending on its characteristics.

Global Electricity Demand and Clean Energy Source Growth Scenario

It is impossible to overstate the importance of energy.Just thinking about where humanity would be without it may be enough to demonstrate this point.Like in the past,energy will play a vital role in shaping future industries,cities,nations,and the world.That is why we believe that energy is a critical factor in shaping future paradigms in any target entity or world.To have a better understanding of the role that energy plays in the world today and in the future,in this article,we briefly look at the definition of energy and its different forms,and review some data related to energy consumption in the world and the United States.Furthermore,as a source of clean energy,we believe the future of nuclear power technology,despite the challenges it faces,is an important option for this country and the rest of the world to meet future energy needs without emitting CO(carbon monoxide)and CO2(carbon dioxide),or other GHGs(greenhouse gases),and other atmospheric pollutants and it is more efficient among its other comparable sources of renewable energies,such as solar,wind,etc.Globally,renewables made up 29 percent of electricity generation in 2020,much of it from hydro-power(16.8 percent).A record amount of over 256 GW of renewable power capacity was added globally during 2020 and continues to be the focal point for climate and energy solutions.Demand for electricity is direct function of population growth globally and is also driven by the present century’s extraordinary technological developments.

Application of a Bayesian Network Complex System Model Examining the Importance of Customer-Industry Engagement to Peak Electricity Demand Reduction

This paper explores the importance of customer-industry engagement (CIE) to peak energy demand by means of a newly developed Bayesian Network (BN) complex systems model entitled the Residential Electricity Peak Demand Model (REPDM). The REPDM is based on a multi-disciplinary perspective designed to solve the complex problem of residential peak energy demand. The model provides a way to conceptualise and understand the factors that shift and reduce consumer demand in peak times. To gain insight into the importance of customer-industry engagement in affecting residential peak demand, this research investigates intervention impacts and major influences through testing five scenarios using different levels of customer-industry engagement activities. Scenario testing of the model outlines the dependencies between the customer-industry engagement interventions and the probabilities that are estimated to govern the dependencies that influence peak demand. The output from the model shows that there can be a strong interaction between the level of CIE activities and interventions. The influence of CIE activity can increase public and householder support for peak reduction and the model shows how the economic, technical and social interventions can achieve greater peak demand reductions when well-designed with appropriate levels of CIE activities.

Electricity demand forecasting at distribution and household levels using explainable causal graph neural network

Forecasting electricity demand is an essential part of the smart grid to ensure a stable and reliable power grid. With the increasing integration of renewable energy resources into the grid, forecasting the demand for electricity is critical at all levels, from the distribution to the household. Most existing forecasting methods, however, can be considered black-box models as a result of deep digitalization enablers, such as deep neural networks, which remain difficult to interpret by humans. Moreover, capture of the inter-dependencies among variables presents a significant challenge for multivariate time series forecasting. In this paper we propose eXplainable Causal Graph Neural Network (X-CGNN) for multivariate electricity demand forecasting that overcomes these limitations. As part of this method, we have intrinsic and global explanations based on causal inferences as well as local explanations based on post-hoc analyses. We have performed extensive validation on two real-world electricity demand datasets from both the household and distribution levels to demonstrate that our proposed method achieves state-of-the-art performance.

Electricity demand, GDP and employment： evidence from Italy

This paper applies time series methodologies to examine the causal relationship among electricity demand, real per capita GDP and total labor force for Italy from 1970 to 2009. After a brief introduction, a survey of the economic literature on this issue is reported, before discussing the data and introducing the econometric techniques used. The results of estimation indicate that one cointegrating relationship exists among these variables. This equilibrium relation implies that, in the long-run, GDP and labor force are correlated negatively, as well as GDP and electricity. Moreover, there is a bi-directional Granger causality flow between real per capita GDP and electricity demand; while labor force does not Granger- cause neither real per capita GDP nor electricity demand. This implies that electricity demand and economic growth are jointly determined at the same time for the Italian case. The forecast error variance decomposition shows that forecast errors in real per capita GDP are mainly caused by the uncertainty in GDP itself, while forecast errors in labor force are mainly resulted from the labor force itself, although aggregate income and electricity are important, too.

Guest editorial:special section on managing electricity demand

The interest in managing electricity demand surfaced in earnest during the 1970s as economic,political,social,technological,and resource supply factors combined to change the electricity sectors’operating environment and its outlook for the future.Ever since then,a successive series of concepts have evolved as an effective way of mitigating these risks including:demand-side management(DSM),demand response(DR),and transactive energy.

Integrated Multi-Head Self-Attention Transformer model for electricity demand prediction incorporating local climate variables

This paper develops a trustworthy deep learning model that considers electricity demand(G)and local climate conditions.The model utilises Multi-Head Self-Attention Transformer(TNET)to capture critical information from𝐻,to attain reliable predictions with local climate(rainfall,radiation,humidity,evaporation,and maximum and minimum temperatures)data from Energex substations in Queensland,Australia.The TNET model is then evaluated with deep learning models(Long-Short Term Memory LSTM,Bidirectional LSTM BILSTM,Gated Recurrent Unit GRU,Convolutional Neural Networks CNN,and Deep Neural Network DNN)based on robust model assessment metrics.The Kernel Density Estimation method is used to generate the prediction interval(PI)of electricity demand forecasts and derive probability metrics and results to show the developed TNET model is accurate for all the substations.The study concludes that the proposed TNET model is a reliable electricity demand predictive tool that has high accuracy and low predictive errors and could be employed as a stratagem by demand modellers and energy policy-makers who wish to incorporate climatic factors into electricity demand patterns and develop national energy market insights and analysis systems.

Prediction of office building electricity demand using artificial neural network by splitting the time horizon for different occupancy rates

Due to the impact of occupants’activities in buildings,the relationship between electricity demand and ambient temperature will show different trends in the long-term and short-term,which show seasonal variation and hourly variation,respectively.This makes it difficult for conventional data fitting methods to accurately predict the long-term and short-term power demand of buildings at the same time.In order to solve this problem,this paper proposes two approaches for fitting and predicting the electricity demand of office buildings.The first proposed approach splits the electricity demand data into fixed time periods,containing working hours and non-working hours,to reduce the impact of occupants’activities.After finding the most sensitive weather variable to non-working hour electricity demand,the building baseload and occupant activities can be predicted separately.The second proposed approach uses the artificial neural network(ANN)and fuzzy logic techniques to fit the building baseload,peak load,and occupancy rate with multi-variables of weather variables.In this approach,the power demand data is split into a narrower time range as no-occupancy hours,full-occupancy hours,and fuzzy hours between them,in which the occupancy rate is varying depending on the time and weather variables.The proposed approaches are verified by the real data from the University of Glasgow as a case study.The simulation results show that,compared with the traditional ANN method,both proposed approaches have less root-mean-square-error(RMSE)in predicting electricity demand.In addition,the proposed working and non-working hour based regression approach reduces the average RMSE by 35%,while the ANN with fuzzy hours based approach reduces the average RMSE by 42%,comparing with the traditional power demand prediction method.In addition,the second proposed approach can provide more information for building energy management,including the predicted baseload,peak load,and occupancy rate,without requiring additional building parameters.

Generating hourly electricity demand data for large-scale single-family buildings by a decomposition-recombination method

Household electricity demand has substantial impacts on local grid operation,energy storage and the energy per-formance of buildings.Hourly demand data at district or urban level helps stakeholders understand the demand patterns from a granular time scale and provides robust evidence in energy management.However,such type of data is often expensive and time-consuming to collect,process and integrate.Decisions built upon smart meter data have to deal with challenges of privacy and security in the whole process.Incomplete data due to confiden-tiality concerns or system failure can further increase the difficulty of modeling and optimization.In addition,methods using historical data to make predictions can largely vary depending on data quality,local building envi-ronment,and dynamic factors.Considering these challenges,this paper proposes a statistical method to generate hourly electricity demand data for large-scale single-family buildings by decomposing time series data and recom-bining them into synthetics.The proposed method used public data to capture seasonality and the distribution of residuals that fulfill statistical characteristics.A reference building was used to provide empirical parameter settings and validations for the studied buildings.An illustrative case in a city of Sweden using only annual total demand was presented for deploying the proposed method.The results showed that the proposed method can mimic reality well and represent a high level of similarity to the real data.The average monthly error for the best month reached 15.9%and the best one was below 10%among 11 tested months.Less than 0.6%improper synthetic values were found in the studied region.

Double Pressure on Power Grids——The demand for electricity is dropping while the electricity price is not adjusted properly

Since October 2008,China's social consumption of electricity had,for the first time,grown negatively compared to the same period of the previous year,and in November the negative growth range further expanded. The major pressure faced by the electricity industry has now turned from the contradiction between coal and electricity to electricity quantity. This is undoubtedly a true and new test to electricity enterprises which get used to high growth but are now suffering great losses. The reform of electricity system has already been in great difficulties and now is getting into a more serious situation. In order to help readers improve their knowledge and understanding of the current tough situation faced by the electricity industry and discuss how to alleviate and get through the difficulty resulted from the economic crisis "encountered once every one hundred years" by joint efforts of all parties concerned,a Seminar on Crisis and Countermeasures for Electricity Industry was held on November 20,2008. Here are some extracts from the speeches of four experts.

The Concept of MDSA （Macro Demand Spatial Approach） on Spatial Demand Forecasting for Main Development Area in Transmission Planning

MDSA （macro demand spatial approach） is an approach introduced in long time electricity demand forecasting considering location. It will be used at transmission planning and policy decision on electricity infrastructure development in a region. In the model, MDSA combined with PCA （principal component analysis） and QA （qualitative analysis） to determine main development area in region and the variables that affecting electricity demand in there. Main development area is an area with industrial domination as a driver of economic growth. The electricity demand driver variables are different for type of electricity consumer. However, they will be equal for main development areas. The variables which have no significant effect can be reduced by using PCA. The generated models tested to assess whether it still at the range of confidence level of electricity demand forecasting. At the case study, generated model for main development areas at South Sumatra Subsystem as a part of Sumatra Interconnection System is still in the range of confidence level. Thus, MDSA can be proposed as alternative approach in transmission planning that considering location.

Energy Sources Driven Electricity Production: A Global Tactical and Strategical Paradigm

Energy forecasting for electricity productivity is the process of applying statistics with possible Quantum or Classical Computing with help from new innovative techniques offered by artificial intelligence to make predictions about consumption levels.This kind of computation presents corresponding utility costs in both the tactical and strategical or short term and long term.Energy forecasting models take into account historical data,trends,weather inputs,tariff structures,and occupancy schedules in the urban city due to population growth,etc.to make predictions.Additionally,energy forecasting as future paradigm is driven by electricity production demand and it is a cost-effective technique to predict future energy needs,which is a paradigm to achieve demand and supply chain equilibrium based on available energy both renewable and non-renewable sources.

The use of permanent magnet motor for Tesla electric vehicle stimulates the demand for rare earth Nd

On March 13th,Reuters reported that the long run version of Tesla Model 3 will use permanent magnet motors.One of the materials for this type of motor is rare earth metal neodymium,which will further increase the supply pressure of neodymium.Governments around the world are committed to reducing the harmful emissions produced by fossil fuel cars,pushing up demand for electric vehicles

A new grey forecasting model based on BP neural network and Markov chain

A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is equivalent to the time response model, was proved by analyzing the features of grey forecasting model（GM（1,1））. Based on this, the differential equation parameters were included in the network when the BP neural network was constructed, and the neural network was trained by extracting samples from grey system＇s known data. When BP network was converged, the whitened grey differential equation parameters were extracted and then the grey neural network forecasting model （GNNM（1,1）） was built. In order to reduce stochastic phenomenon in GNNM（1,1）, the state transition probability between two states was defined and the Markov transition matrix was established by building the residual sequences between grey forecasting and actual value. Thus, the new grey forecasting model（MNNGM（1,1）） was proposed by combining Markov chain with GNNM（1,1）. Based on the above discussion, three different approaches were put forward for forecasting China electricity demands. By comparing GM（1, 1） and GNNM（1,1） with the proposed model, the results indicate that the absolute mean error of MNNGM（1,1） is about 0.4 times of GNNM（1,1） and 0.2 times of GM（I, 1）, and the mean square error of MNNGM（1,1） is about 0.25 times of GNNM（1,1） and 0.1 times of GM（1,1）.

System Dynamics Forecasting on Taiwan Power Supply Chain

This research aims to study the sustainability of Taiwan power supplychain based on system dynamics forecasting. The paper tries to investigate electricity shortage effects not only on the industrial side, but also from the standpointof society. In our model, different forecasting methods such as linear regression,time series analysis, and gray forecasting are also considered to predict the parameters. Further tests such as the structure, dimension, historical fit, and sensitivityof the model are also conducted in this paper. Through analysis forecasting result,we believe that the demand for electricity in Taiwan will continue to increase to acertain level for a period of time in the future. This phenomenon is closely relatedto Taiwan’s economic development, especially industrial development. We alsopoint out that electricity prices in Taiwan do not match with high industrialdemand, and that prices are still slightly low. Finally, the future growth trend ofTaiwan’s electricity demand has not changed, and ensuring adequate supply tomeet electricity demand to prevent potential power shortages will pose somedifficulty.

Study on Relation between Industrial Structure and Grid Load in Shanxi Province

The section of electric power is the foundation of national economy. The paper analyzes the relation between industrial structure and grid load in Shanxi province, and finds out that electricity demand and grid load relate linearly to value added of industry. In the end, the paper predicts electricity demand and grid load via the model.

Optimal Operation of Integrated Energy Systems Subject to Coupled Demand Constraints of Electricity and Natural Gas

This paper proposes a hybrid multi-objective optimization and game-theoretic approach(HMOGTA)to achieve the optimal operation of integrated energy systems(IESs)consisting of electricity and natural gas(E&G)utility networks,multiple distributed energy stations(DESs),and multiple energy users(EUs).The HMOGTA aims to solve the coordinated operation strategy of the electricity and natural gas networks considering the demand characteristics of DESs and EUs.In the HMOGTA,a hierarchical Stackelberg game model is developed for generating equilibrium strategies of DESs and EUs in each district energy network(DEN).Based on the game results,we obtain the coupling demand constraints of electricity and natural gas(CDCENs)which reflect the relationship between the amounts and prices of electricity and cooling(E&C)that DESs purchase from utility networks.Furthermore,the minimization of conflicting costs of E&G networks considering the CDCENs are solved by a multi-objective optimization method.A case study is conducted on a test IES composed of a 20-node natural gas network,a modified IEEE 30-bus system,and 3 DENs,which verifies the effectiveness of the proposed HMOGTA to realize fair treatment for all participants in the IES.

Optimization PV/Batteries System: Application in Wouro Kessoum Village Ngaoundere Cameroon

This paper presents the optimization of the PV/battery system including extrapolation of the electrical demand. Matlab software was chosen to implement the algorithm. PVC, the number of PV modules and battery capacity increase with increasing electrical demand. This makes it possible to predict the device according to the electrical demand. Particle swarm optimization is used to minimize the total cost of the system over 20 years. The average cost of energy is $0.369/kWh.

Our Daily Life Dependency Driven by Renewable and Nonrenewable Source of Energy

Our dependency on energy is so vital that it makes it difficult to imagine how humans can live on our planet earth without it.The demand for electricity,for example,is directly related to the growth of the population worldwide,and presently,to meet this demand,we need both renewable and nonrenewable energy.While nonrenewable energy has its shortcomings(negative impact on climate change,for example),renewable energy is not enough to address the ever-changing demand for energy.One way to address this need is to become more innovative,use technology more effectively,and be aware of the costs associated with different sources of renewable energy.In the case of nuclear power plants,new innovative centered around small modular reactors(SMRs)of generation 4th of these plants make them safer and less costly to own them as well as to protect them via means of cyber-security against any attack by smart malware.Of course,understanding the risks and how to address them is an integral part of the study.Natural sources of energy,such as wind and solar,are suggesting other innovating technical approaches.In this article,we are studying these factors holistically,and details have been laid out in a book by the authors’second volume of series title as Knowledge Is Power in Four Dimensions under Energy subtitle.

Renewable energy in Benin: current situation and future prospects

To provide clean energy at a lower cost to their citizens,all nations of the world are striving to increase their energy production in an environmentally friendly way.Benin has also joined this dynamic by considerably increasing its green energy production efforts in recent years.The country has a huge undeveloped renewable-energy(RE)potential that can contribute considerably to its national energy production capacity.This paper summarizes the current RE situation in Benin and examines its future prospects.The current energy situation of the country is discussed,followed by an examination of its electricity demand-and-supply situation.The country has been found to depend heavily on natural gas and petroleum products from neighbouring countries and has~41%of national electricity access.However,the government is taking considerable steps to implement RE projects in the country.The study analyzes government targets in the energy sector with existing policies and institutional frameworks.Recommendations are made for the benefit of the government,the private sector and other actors in order to developing the RE potential of Benin.